The National Institutes of Health awarded more than $12 million to the University of Hawaiʻi Cancer Center and the John A. Burns School of Medicine to establish a research center dedicated to advancing artificial intelligence and data science in medicine. The five-year grant, announced on June 4, will create the Pacific Center for Artificial Intelligence and Data Science in Medicine (PAC-AID) to accelerate biomedical discoveries for Hawaiʻi, the Pacific region, and beyond.
PAC-AID will serve as a central hub integrating AI into biomedical research, with a new Medical AI Core (MedAI Core) providing high-performance computing resources and AI expertise. The center, funded through the NIH's Centers of Biomedical Research Excellence (COBRE) program, will renovate the UH Cancer Center Data Center to support these capabilities. The center's focus on applying machine learning to complex health data reflects the broader field of AI for Science & Research, where computational methods accelerate discovery.
Principal investigators John Shepherd, chief scientific officer at the UH Cancer Center, and Youping Deng, co-director of genomics and bioinformatics, will lead the center. Shepherd is also the B.H. and Alice C. Beams Endowed Professor in Cancer Research at JABSOM; Deng directs the Bioinformatics Core Facility there. "AI has the potential to unlock major medical breakthroughs and help people live healthier lives, and we need to take advantage of it," said U.S. Sen. Brian Schatz, who helped secure the grant. "This new funding will help Hawai'i continue to attract top-tier talent and develop treatments and cures that will benefit people across the state."
Initial research projects and investigator development
The center directly funds four inaugural research projects: Kevin Cassel's work using full-body imaging to triage skin lesions, Elizabeth Nakasone's study of pancreatic cancer in Native Hawaiian and Japanese populations, Jonathan Huang's modeling of environmental toxicant effects on fetal development, and Yiqiang Zhang's identification of genetic traits in congenital heart disease. A Pilot Projects Program will support more than eight additional locally relevant studies. By building a Medical AI Core, PAC-AID demonstrates how AI for Healthcare can be tailored to regional health needs.
Beyond funding research, the award establishes a workforce development mechanism to support early-stage faculty members across the University of Hawaiʻi and Pacific Island institutions. Shepherd said PAC-AID is designed to develop six to eight early-stage faculty who will use AI and data science to address cancer and chronic disease outcomes in one of the nation's most medically underserved regions.
Economic and research impact
Shepherd outlined the expected return: "Our benchmark for success is that the funded faculty projects, 4 initially and 2-4 more when those graduate in year 3, and will later achieve independent NIH R01 funding at an estimated $3.25 million per award. That represents a projected $19.5 million in additional federal research funding returned to the State of Hawaiʻi (6 funded R01s) on top of the initial $12 million COBRE award itself - a combined potential economic and research impact of more than $31 million for Hawaiʻi's research and healthcare workforce. These estimates are for the first 5-year period."
"By the end of this project, we expect to have a nationally competitive Medical AI Core, four independently funded research leaders, and more than 10 pilot projects," Deng said. "Through these efforts, alongside workshops and collaborative research opportunities, we will significantly strengthen Hawaiʻi's capacity for AI-enabled biomedical research and innovation to address important health challenges in our region and beyond."
Why this matters for science and research professionals
PAC-AID offers a replicable model of how targeted federal investment can build local AI infrastructure and investigator pipelines in underserved regions. The center's emphasis on developing a Medical AI Core, funding early-career faculty, and launching pilot studies that address specific population health disparities demonstrates a practical pathway from grant to independent research programs. For researchers and data scientists in biomedicine, the initiative shows that integrating AI into biomedical research - from imaging analysis to genomic studies - requires not only computational resources but also sustained mentorship and collaborative frameworks that turn complex data into actionable health solutions.
Your membership also unlocks:
